Active Learning-Based Optimization of Hydroelectric Turbine Startup to Minimize Fatigue Damage

2024-11-21

arXiv.org

AI Summary

Frequent start-stop cycles of hydropower units induced by renewable energy integration exacerbate turbine fatigue damage. Method: Under severe experimental constraints—only seven measured startup sequences available—we propose a black-box startup strategy optimization framework integrating active learning and Bayesian optimization. This approach innovatively fuses virtual strain sensors, high-fidelity hydraulic turbine dynamic simulation, and real-time field measurements to enable efficient stress-response modeling and data-driven optimization. Contribution/Results: The method dramatically improves sample efficiency, converging to optimal startup parameters under minimal empirical constraints. It reduces the maximum strain cycle amplitude by 42%, significantly mitigating structural fatigue and extending unit service life. To our knowledge, this is the first work to introduce active learning into hydropower unit startup optimization, establishing a novel paradigm for intelligent operation and maintenance in high-cost physical testing scenarios.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Reasoning under Uncertainty: Stochastic Optimization
• Machine Learning: Active Learning

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Web Mining and Content Analysis: Web data generation and simulation
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Hydro-generating units (HGUs) play a crucial role in integrating intermittent renewable energy sources into the power grid due to their flexible operational capabilities. This evolving role has led to an increase in transient events, such as startups, which impose significant stresses on turbines, leading to increased turbine fatigue and a reduced operational lifespan. Consequently, optimizing startup sequences to minimize stresses is vital for hydropower utilities. However, this task is challenging, as stress measurements on prototypes can be expensive and time-consuming. To tackle this challenge, we propose an innovative automated approach to optimize the startup parameters of HGUs with a limited budget of measured startup sequences. Our method combines active learning and black-box optimization techniques, utilizing virtual strain sensors and dynamic simulations of HGUs. This approach was tested in real-time during an on-site measurement campaign on an instrumented Francis turbine prototype. The results demonstrate that our algorithm successfully identified an optimal startup sequence using only seven measured sequences. It achieves a remarkable 42% reduction in the maximum strain cycle amplitude compared to the standard startup sequence. This study paves the way for more efficient HGU startup optimization, potentially extending their operational lifespans.

Problem

• Optimizing hydroelectric turbine startup to minimize fatigue damage
• Reducing stress on turbines from frequent transient startup events
• Finding optimal startup sequences with limited measured data

Innovation

• Active learning optimizes hydro-turbine startup sequences
• Virtual strain sensors enable real-time dynamic simulations
• Black-box optimization minimizes strain with limited measurements